Texture Discrimination Based upon an Assumed Stochastic Texture Model.

Abstract

A new approach to texture discrimination is described. This approach is based upon an assumed stochastic model for texture in imagery and is an approximation to the statistically optimum maximum likelihood classifier. The construction and properties of the stochastic texture model are described and a digital filtering implementation of the resulting maximum likelihood texture discriminant is provided. The efficacy of this approach is demonstrated through experimental results obtained with simulated texture data. A comparison is provided with more conventional texture discriminants under identical conditions. The implications to texture discrimination in real-world imagery are discussed. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Mar 01, 1980
Accession Number
ADA084047

Entities

People

  • Acie L. Vickers
  • James W. Modestino
  • Robert W. Fries

Organizations

  • Syracuse University

Tags

Communities of Interest

  • Air Platforms
  • Energy and Power Technologies
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Computer Programs
  • Data Science
  • Databases
  • Detectors
  • Digital Filters
  • Filters
  • Filtration
  • Frequency Response
  • Image Processing
  • Information Processing
  • Information Science
  • Mathematical Filters
  • Order Statistics
  • Probability
  • Random Variables
  • Two Dimensional

Readers

  • Computer Vision.
  • Regression Analysis.